[1]}, [2]} study the holistic influence maximization (HIM) query problem, which aims to find a minimum set of users whose holistic influence can cover all target users in the network. They evaluate the influence diffusion by considering the social connection, spatial connection, and preference-based connection, rather than only taking social connection into account. Specifically, they propose a holistic influence diffusion model, named HIM model, under the basis of the IC model that takes into account both cyber and physical user interactions. To answer the HIM query, they propose a basic algorithm that uses the reverse \(k\)  nearest neighbors technique to evaluate the offline influence spread and adopt the sketch-based approach to calculate the influence of users under the HIM model. In addition, they develop an index-based method to deal with large networks.
